Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus
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Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus. / Pena, Michelle J; Heinzel, Andreas; Rossing, Peter; Parving, Hans-Henrik; Dallmann, Guido; Rossing, Kasper; Andersen, Steen; Mayer, Bernd; Heerspink, Hiddo J L.
I: Journal of Translational Medicine, Bind 14, 203, 05.07.2016.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus
AU - Pena, Michelle J
AU - Heinzel, Andreas
AU - Rossing, Peter
AU - Parving, Hans-Henrik
AU - Dallmann, Guido
AU - Rossing, Kasper
AU - Andersen, Steen
AU - Mayer, Bernd
AU - Heerspink, Hiddo J L
PY - 2016/7/5
Y1 - 2016/7/5
N2 - BACKGROUND: Individual patients show a large variability in albuminuria response to angiotensin receptor blockers (ARB). Identifying novel biomarkers that predict ARB response may help tailor therapy. We aimed to discover and validate a serum metabolite classifier that predicts albuminuria response to ARBs in patients with diabetes mellitus and micro- or macroalbuminuria.METHODS: Liquid chromatography-tandem mass spectrometry metabolomics was performed on serum samples. Data from patients with type 2 diabetes and microalbuminuria (n = 49) treated with irbesartan 300 mg/day were used for discovery. LASSO and ridge regression were performed to develop the classifier. Improvement in albuminuria response prediction was assessed by calculating differences in R(2) between a reference model of clinical parameters and a model with clinical parameters and the classifier. The classifier was externally validated in patients with type 1 diabetes and macroalbuminuria (n = 50) treated with losartan 100 mg/day. Molecular process analysis was performed to link metabolites to molecular mechanisms contributing to ARB response.RESULTS: In discovery, median change in urinary albumin excretion (UAE) was -42 % [Q1-Q3: -69 to -8]. The classifier, consisting of 21 metabolites, was significantly associated with UAE response to irbesartan (p < 0.001) and improved prediction of UAE response on top of the clinical reference model (R(2) increase from 0.10 to 0.70; p < 0.001). In external validation, median change in UAE was -43 % [Q1-Q35: -63 to -23]. The classifier improved prediction of UAE response to losartan (R(2) increase from 0.20 to 0.59; p < 0.001). Specifically ADMA impacting eNOS activity appears to be a relevant factor in ARB response.CONCLUSIONS: A serum metabolite classifier was discovered and externally validated to significantly improve prediction of albuminuria response to ARBs in diabetes mellitus.
AB - BACKGROUND: Individual patients show a large variability in albuminuria response to angiotensin receptor blockers (ARB). Identifying novel biomarkers that predict ARB response may help tailor therapy. We aimed to discover and validate a serum metabolite classifier that predicts albuminuria response to ARBs in patients with diabetes mellitus and micro- or macroalbuminuria.METHODS: Liquid chromatography-tandem mass spectrometry metabolomics was performed on serum samples. Data from patients with type 2 diabetes and microalbuminuria (n = 49) treated with irbesartan 300 mg/day were used for discovery. LASSO and ridge regression were performed to develop the classifier. Improvement in albuminuria response prediction was assessed by calculating differences in R(2) between a reference model of clinical parameters and a model with clinical parameters and the classifier. The classifier was externally validated in patients with type 1 diabetes and macroalbuminuria (n = 50) treated with losartan 100 mg/day. Molecular process analysis was performed to link metabolites to molecular mechanisms contributing to ARB response.RESULTS: In discovery, median change in urinary albumin excretion (UAE) was -42 % [Q1-Q3: -69 to -8]. The classifier, consisting of 21 metabolites, was significantly associated with UAE response to irbesartan (p < 0.001) and improved prediction of UAE response on top of the clinical reference model (R(2) increase from 0.10 to 0.70; p < 0.001). In external validation, median change in UAE was -43 % [Q1-Q35: -63 to -23]. The classifier improved prediction of UAE response to losartan (R(2) increase from 0.20 to 0.59; p < 0.001). Specifically ADMA impacting eNOS activity appears to be a relevant factor in ARB response.CONCLUSIONS: A serum metabolite classifier was discovered and externally validated to significantly improve prediction of albuminuria response to ARBs in diabetes mellitus.
KW - Journal Article
U2 - 10.1186/s12967-016-0960-3
DO - 10.1186/s12967-016-0960-3
M3 - Journal article
C2 - 27378474
VL - 14
JO - Journal of Translational Medicine
JF - Journal of Translational Medicine
SN - 1479-5876
M1 - 203
ER -
ID: 172818155